Coming soon · Book Profile
Predictive HR Analytics
A practical, Excel-driven guide that teaches HR professionals with no statistical background how to use predictive analytics to forecast employee behavior and drive measurable business results.
A profile of this book is on the way.
What it’s about
Predictive HR Analytics demystifies data science for HR practitioners by teaching them to run real predictive models—decision trees, correlation, multiple regression, logistic regression, and Chi-Square tests—using only Microsoft Excel, with step-by-step screenshots. Anchored in the five-step ARHAT framework (Ask questions, Review literature, Hypothesis formulation, Analyze data, Tell the story) and packed with real-world case studies from Best Buy, Google, Xerox, Deloitte, Nielsen, and ISS, the book shows how to predict who is likely to leave, which candidates will succeed, how engagement drives revenue and shareholder returns, how diversity impacts EBIT, and how training pays off. It spans the entire HR analytics scope—from engagement and turnover to compensation, diversity, learning, recruitment, and safety—while teaching data storytelling and visualization so insights actually change decisions, all without expensive software or months of programming.
The through-line
- Who it’s for
- An HR professional or people analytics practitioner who wants to drive measurable business results and gain a competitive edge through data, but lacks a statistical or programming background.
- The problem
- They need to predict employee behavior (who will leave, who will perform, how engagement affects revenue) but don't have a structured framework or affordable, learnable tools. They feel intimidated by complex statistics, overwhelmed by unstructured problems, and worried about being excluded from key strategic decisions.
- The plan
- Adopt the five-step ARHAT framework: Ask questions, Review literature, formulate Hypotheses, Analyze data, Tell the story.
- Learn Excel statistical techniques (decision trees, correlation, multiple and logistic regression, Chi-Square) with step-by-step screenshots.
- Apply the techniques to specific HR domains using the book's real-world case studies and worked examples.
- Manage stakeholders, prioritize quick-win projects, and ensure legal/privacy compliance.
- Communicate insights through data storytelling and visualization to drive action.
- The payoff
- You can predict turnover, performance, and engagement impact yourself rather than relying on consultants or gut feel. · You establish credibility with business heads by delivering quick wins tied to company KPIs. · You see what is invisible to others—the behaviors that differentiate your best employees—creating competitive advantage.
See our guide
Related profiles we’ve built
- Beyond Hr Boudreau Ramstad →
- Compensating Your Employees Fairly →
- Handbook of Regression Modeling in People Analytics →
- Transformative HR: How Great Companies Use Evidence-Based Change for Sustainable Advantage →
- Work Rules! →
- The basic principle of people analytics learn how to use hr data to drive better outcomes for your business and employees →
Additional reading
- Jack: Straight from the Gut · Jack Welch
The book critiques the widespread, unthinking adoption of GE's '20-70-10' performance ranking system as a prime example of management fad-following, which talentship aims to replace with context-specific, logical analysis.
- Moneyball: The Art of Winning an Unfair Game · Michael Lewis
Used as a key analogy for talentship. It demonstrates how a decision-science approach can identify undervalued, pivotal capabilities to create a competitive advantage, just as talentship aims to do for organizations.
- Work Rules! · Laszlo Bock
Written by Google's former head of People Operations, it provides detailed insights into how Google uses a data-driven approach for hiring and management, a core theme of this book.
- Data Strategy: How to Profit from a World of Big Data, Analytics and the Internet of Things · Bernard Marr
The author's own book, recommended for readers who want more detailed guidance on creating the data strategy that is presented as the foundational first step for data-driven HR.
- High Output Management · Andy Grove
Referenced in the book as an example of how to think analytically and build a business case for a management decision, specifically regarding the ROI of a manager training their own team.
- The Power of People: Learn How Successful Organizations Use Workforce Analytics to Improve Business Performance · Guenole, N., Ferrar, J., & Feinzig, S.
Cited in the book, this is a foundational text that aligns with the book's core theme of using workforce analytics to drive tangible business improvements.
- Human Capital Analytics: How to Harness the Potential of Your Organization's Greatest Asset · Pease, G., Byerly, B., & Fitz-enz, J.
Cited in the book and written by a pioneer in the field, this work provides a comprehensive view on human capital analytics, complementing this book's hands-on manual approach.
- Handbook of regression modeling in people analytics · Keith McNulty
The author's previous book, likely providing foundational quantitative skills for readers who are new to programming or data analysis in R.
- ggplot2: Elegant graphics for data analysis · Hadley Wickham
The definitive guide to the `ggplot2` package in R, which is the foundation for the `ggraph` network visualization package used extensively in the book.
- Stanford Large Network Dataset Collection (SNAP) · Stanford Network Analysis Project
A key public resource with a wide range of large network datasets, recommended by the author for further practice and exploration beyond the book's examples.